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Proceedings of the International Association of Hydrological Sciences An open-access publication for refereed proceedings in hydrology
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Volume 368
Proc. IAHS, 368, 251–256, 2015
https://doi.org/10.5194/piahs-368-251-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Proc. IAHS, 368, 251–256, 2015
https://doi.org/10.5194/piahs-368-251-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  06 May 2015

06 May 2015

POME-copula for hydrological dependence analysis

D. Liu1, D. Wang1, L. Wang2, Y. Chen3, X. Chen3, and S. Gu4 D. Liu et al.
  • 1Laboratory of Surficial Geochemistry, MOE, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, China
  • 2School of Geographic and Oceanographic sciences, Nanjing University, Nanjing, China
  • 3School of Hydrology and Water Resources, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China
  • 4Shanghai Hydrology Administration, Shanghai, China

Keywords: The principle of maximum entropy, copula, dependence analysis, Shannon entropy, marginal distribution

Abstract. Hydrological multivariate analysis has been widely studied using copula-based modelling, in which marginal distribution inference is one of the key issues. The main object of this study is to discuss the applicability of the principle of maximum entropy (POME) in marginal distribution inference, thus to develop a POME-copula framework to analyse the dependence of hydrological variables. Marginal distributions are derived with the POME approach before bivariate copulas constructed with corresponding parameters estimated by the dependence of the derived margins. The proposed POME-copula has been employed in hydrological dependence analyses, with the annual maximum streamflow and water level collected from the Yangtze River, and the monthly streamflow from the Yellow River. Results show that the POME-copula method performs well in capturing dependence patterns of various hydrological variables.

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